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https://github.com/wassname/isokl_steering_calibration.git
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wassname
37 lines
1.4 KiB
Python
37 lines
1.4 KiB
Python
"""Debug: verify pmass measurement on un-steered baseline."""
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
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model = AutoModelForCausalLM.from_pretrained(
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"Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.float32
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).eval()
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prefill = '\n{"choice": '
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schema = (
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'Think briefly, then answer immediately and only with: '
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'{"choice": true} or {"choice": false}. Do not output 1 or 0.'
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)
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q = "Is the Eiffel Tower located in Paris, France?"
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msgs = [{"role": "user", "content": schema + " " + q}]
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chat = tok.apply_chat_template(msgs, add_generation_prompt=True, tokenize=False)
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full = chat + prefill
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ids = tok(full, return_tensors="pt").input_ids
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print(f"prompt len={ids.shape[1]} tokens")
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with torch.no_grad():
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logits = model(ids).logits[0, -1]
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probs = torch.softmax(logits, dim=-1)
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top = probs.topk(10)
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print("\nTop-10 next tokens after full prefill (NO steer, NO rollout):")
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for p, i in zip(top.values, top.indices):
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print(f" id={int(i):6d} p={float(p):.4f} tok={tok.decode([int(i)])!r}")
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a_ids = [16, 1866, 2514, 830, 3007] # 1 true True ' true' ' True'
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b_ids = [15, 3849, 4049] # 0 false False
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sa = float(probs[a_ids].sum())
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sb = float(probs[b_ids].sum())
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print(f"\nsum a_ids (true/1)={sa:.4f} sum b_ids (false/0)={sb:.4f} pmass={sa+sb:.4f}")
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print(f"argmax: id={int(probs.argmax())} tok={tok.decode([int(probs.argmax())])!r}")
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